SOTAVerified

Gaussian Processes

Gaussian Processes is a powerful framework for several machine learning tasks such as regression, classification and inference. Given a finite set of input output training data that is generated out of a fixed (but possibly unknown) function, the framework models the unknown function as a stochastic process such that the training outputs are a finite number of jointly Gaussian random variables, whose properties can then be used to infer the statistics (the mean and variance) of the function at test values of input.

Source: Sequential Randomized Matrix Factorization for Gaussian Processes: Efficient Predictions and Hyper-parameter Optimization

Papers

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Showing 441–450 of 1963 papers

TitleStatusHype
Bandits for Learning to Explain from Explanations—0
Deep Factors with Gaussian Processes for Forecasting—0
Deep Feature Gaussian Processes for Single-Scene Aerosol Optical Depth Reconstruction—0
Deep Gaussian Covariance Network—0
BARK: A Fully Bayesian Tree Kernel for Black-box Optimization—0
Physics Enhanced Data-Driven Models with Variational Gaussian Processes—0
Combining Parametric Land Surface Models with Machine Learning—0
Design of Experiments for Verifying Biomolecular Networks—0
Deep Gaussian Processes: A Survey—0
Dialogue manager domain adaptation using Gaussian process reinforcement learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ICKy, periodicRoot mean square error (RMSE)0.03—Unverified